pacman::p_load(sf, tidyverse, spdep, tmap, funModeling)Take Home Exercise 1 - Investigation of Water points in Nigeria
Overview
Water is a crucial resource for humanity. People must have access to clean water in order to be healthy. It promotes a healthy environment, peace and security, and a sustainable economy. However, more than 40% of the world’s population lacks access to enough clean water. According to UN-Water, 1.8 billion people would live in places with a complete water shortage by 2025. One of the many areas that the water problem gravely threatens is food security. Agriculture uses over 70% of the freshwater that is present on Earth.
The severe water shortages and water quality issues are seen in underdeveloped countries. Up to 80% of infections in developing nations are attributed to inadequate water and sanitation infrastructure.
Despite technological advancement, providing rural people with clean water continues to be a key development concern in many countries around the world, especially in those on the continent of Africa.
The spatial patterns of non-functional water points will be shown in this study by using the proper global and local spatial association methodologies. We look at Nigeria’s in this assignment.
Getting Started
First, the required packages are loaded into the R environment . The required packages are sf, tidyverse, spdep, tmap, & funModeling
with the code below:
Spatial Data
The spatial dataset used in this assignment is the Nigeria Level-2 Administrative Boundary spatial dataset downloaded from Center for Humanitarian Data - Nigeria - Subnational Administrative Boundaries
We will load the spatial features by using st_read() from the sf package
As the data we want is in WSG-84 format, we set crs to 4326.
We won’t utilize st transform() at this time because it can result in outputs with missing points after transformation, which would skew our study.
nga = st_read(dsn = "data/geospatial",
layer = "nga_admbnda_adm2_osgof_20190417",
crs = 4326)Reading layer `nga_admbnda_adm2_osgof_20190417' from data source
`D:\Allanckw\ISSS624\Take-Home_Ex1\data\geospatial' using driver `ESRI Shapefile'
Simple feature collection with 774 features and 16 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84#nigeria_lga_sf = st_transform(nigeria_lga_sf, crs=4326) cause missing pointsWe could use st_crs()to verify the coordinate system from the object.
st_crs(nga)Coordinate Reference System:
User input: EPSG:4326
wkt:
GEOGCRS["WGS 84",
ENSEMBLE["World Geodetic System 1984 ensemble",
MEMBER["World Geodetic System 1984 (Transit)"],
MEMBER["World Geodetic System 1984 (G730)"],
MEMBER["World Geodetic System 1984 (G873)"],
MEMBER["World Geodetic System 1984 (G1150)"],
MEMBER["World Geodetic System 1984 (G1674)"],
MEMBER["World Geodetic System 1984 (G1762)"],
MEMBER["World Geodetic System 1984 (G2139)"],
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ENSEMBLEACCURACY[2.0]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Horizontal component of 3D system."],
AREA["World."],
BBOX[-90,-180,90,180]],
ID["EPSG",4326]]Before we start analyzing the data, lets us take a look at some characteristics of the spatial features to have a sense of what we are dealing with. We can use glimpse() to determine to accomplish that
glimpse(nga)Rows: 774
Columns: 17
$ Shape_Leng <dbl> 0.2370744, 0.2624772, 3.0753158, 2.5379842, 0.6871498, 1.06…
$ Shape_Area <dbl> 0.0015239210, 0.0035311037, 0.3268678399, 0.0683785064, 0.0…
$ ADM2_EN <chr> "Aba North", "Aba South", "Abadam", "Abaji", "Abak", "Abaka…
$ ADM2_PCODE <chr> "NG001001", "NG001002", "NG008001", "NG015001", "NG003001",…
$ ADM2_REF <chr> "Aba North", "Aba South", "Abadam", "Abaji", "Abak", "Abaka…
$ ADM2ALT1EN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ ADM2ALT2EN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ ADM1_EN <chr> "Abia", "Abia", "Borno", "Federal Capital Territory", "Akwa…
$ ADM1_PCODE <chr> "NG001", "NG001", "NG008", "NG015", "NG003", "NG011", "NG02…
$ ADM0_EN <chr> "Nigeria", "Nigeria", "Nigeria", "Nigeria", "Nigeria", "Nig…
$ ADM0_PCODE <chr> "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG",…
$ date <date> 2016-11-29, 2016-11-29, 2016-11-29, 2016-11-29, 2016-11-29…
$ validOn <date> 2019-04-17, 2019-04-17, 2019-04-17, 2019-04-17, 2019-04-17…
$ validTo <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ SD_EN <chr> "Abia South", "Abia South", "Borno North", "Federal Capital…
$ SD_PCODE <chr> "NG00103", "NG00103", "NG00802", "NG01501", "NG00302", "NG0…
$ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((7.401109 5...., MULTIPOLYGON (…We can use freq() of the funModeling package to display the distribution of Level 1 administration (Which are states in Nigeria) instead and only zooming in on the micro level when we perform the water point analysis.
freq(data=nga, input = 'ADM1_EN')
ADM1_EN frequency percentage cumulative_perc
1 Kano 44 5.68 5.68
2 Katsina 34 4.39 10.07
3 Oyo 33 4.26 14.33
4 Akwa Ibom 31 4.01 18.34
5 Osun 30 3.88 22.22
6 Borno 27 3.49 25.71
7 Imo 27 3.49 29.20
8 Jigawa 27 3.49 32.69
9 Delta 25 3.23 35.92
10 Niger 25 3.23 39.15
11 Benue 23 2.97 42.12
12 Kaduna 23 2.97 45.09
13 Rivers 23 2.97 48.06
14 Sokoto 23 2.97 51.03
15 Adamawa 21 2.71 53.74
16 Anambra 21 2.71 56.45
17 Kebbi 21 2.71 59.16
18 Kogi 21 2.71 61.87
19 Bauchi 20 2.58 64.45
20 Lagos 20 2.58 67.03
21 Ogun 20 2.58 69.61
22 Cross River 18 2.33 71.94
23 Edo 18 2.33 74.27
24 Ondo 18 2.33 76.60
25 Abia 17 2.20 78.80
26 Enugu 17 2.20 81.00
27 Plateau 17 2.20 83.20
28 Yobe 17 2.20 85.40
29 Ekiti 16 2.07 87.47
30 Kwara 16 2.07 89.54
31 Taraba 16 2.07 91.61
32 Zamfara 14 1.81 93.42
33 Ebonyi 13 1.68 95.10
34 Nasarawa 13 1.68 96.78
35 Gombe 11 1.42 98.20
36 Bayelsa 8 1.03 99.23
37 Federal Capital Territory 6 0.78 100.00774 Local Government Areas (LGA) make up Nigeria’s 37 states, with Kano having the most LGAs overall.
For a meaningful analysis, there are just too many LGAs, both large and little.
Calling ttm() in the tmap package will switch the tmap’s viewing mode to interactive viewing, which will help us better visualize the map. Without this change, the tmap will be too small for any type of analysis. Additionally, we’ll base the map’s plot on States (Level 1 Administration Area)
Given that there are 37 states, we must raise the maximum number of categories from the default value of 30 to 37. Using tmap_options(max.categories = 37), the threshold can be set.
ttm()
tmap_options(max.categories = 37)Now, we are ready to build our map with the functions in the tmap package
tm_shape(nga) +
tm_polygons("ADM1_EN") +
tm_borders(alpha=0.5) +
tm_scale_bar() +
tm_grid (alpha=0.2) +
tm_layout(main.title="Map of Nigeria LGA",
main.title.position="center",
main.title.size=1.2,
legend.height = 0.35,
legend.width = 0.35,
frame = TRUE) Aspatial Data
Cleaning the Data
The aspatial dataset used in this assignment is the water point data exchange dataset found in WPdx Global Data Repositories. Data is filtered on the web portal to only keep Nigeria and the file is saved as NigeriaWaterPoints_Raw.csv
As we are only interested in the functionality of the water point, it is important to capture fields that may affect the functionality
LGA: The area we are interested in
State: The state of the LGA of Nigeria
Functional: Whether it is functional or not
management: who manages it?
Quality: what is the quality?
Water Source Category: where the water came from?
Water Tech Category: What technology is used?
latitude
longitude
To load the raw data file, we use the read_csv function
wpdx_raw = read_csv("data/aspatial/NigeriaWaterPoints_Raw.csv") Most of the columns are irrelevant, so we will perform the following:
keep the columns we want to clean it up by specifying the columns with one to retain with
subsetrenaming the columns using
rename_withReplace all the NA with unknown for columns with NA value present
retain_cols <- c('#clean_adm2', '#clean_adm1', '#status_clean', '#management_clean', '#subjective_quality', '#fecal_coliform_presence', '#water_source_category', '#water_tech_category', '#lat_deg', '#lon_deg' )
new_col_names <- c('LGA', 'State', 'Functional', 'Management', 'Quality', 'presence_of_fecal_coliform', 'Water_Source_Category', 'Water_Tech_Category', 'latitude', 'longitude')
wpdx_clean = subset(wpdx_raw, select = (names(wpdx_raw) %in% retain_cols)) %>% rename_with(~ new_col_names, all_of(retain_cols)) %>%
replace_na(list(Functional = "Unknown", Management = "Unknown", Quality = "Unknown", Water_Source_Category = "Unknown", Water_Tech_Category = "Unknown"))We save the clean file with saveRDS(), the file will be reduced to 1.6MB from the 144MB raw file that we downloaded.
saveRDS(wpdx_clean, "data/aspatial/wpdx_clean.rds")We can then delete the raw file from the project and retrieve the saved RDS file using readRDS()
wpdx_clean = readRDS("data/aspatial/wpdx_clean.rds")Converting csv data into spatial features
We can use st_as_sfto create a dataframe from the longitude (x) and latitude (y) values. The EPSG 4326 code is used as the dataset is referencing WGS84 geographic coordinate system. We could use st_crs()to verify the coordinate system from the object.
wpdx_clean_sf = st_as_sf(wpdx_clean, coords = c("longitude", "latitude"), crs=4326)
st_crs(wpdx_clean_sf)Coordinate Reference System:
User input: EPSG:4326
wkt:
GEOGCRS["WGS 84",
ENSEMBLE["World Geodetic System 1984 ensemble",
MEMBER["World Geodetic System 1984 (Transit)"],
MEMBER["World Geodetic System 1984 (G730)"],
MEMBER["World Geodetic System 1984 (G873)"],
MEMBER["World Geodetic System 1984 (G1150)"],
MEMBER["World Geodetic System 1984 (G1674)"],
MEMBER["World Geodetic System 1984 (G1762)"],
MEMBER["World Geodetic System 1984 (G2139)"],
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ENSEMBLEACCURACY[2.0]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["geodetic latitude (Lat)",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["geodetic longitude (Lon)",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
USAGE[
SCOPE["Horizontal component of 3D system."],
AREA["World."],
BBOX[-90,-180,90,180]],
ID["EPSG",4326]]We can then use glimpse() to verify each field’s data type & available values.
There are 95, 008 water points in the LGAs. The results also shows that the longitude and latitude values have been converted to a geometry object consisting of the longitude and latitude values as points, with both columns now dropped.
glimpse(wpdx_clean_sf)Rows: 95,008
Columns: 8
$ Water_Source_Category <chr> "Unknown", "Well", "Well", "Well", "Well", "Well…
$ Water_Tech_Category <chr> "Tapstand", "Mechanized Pump", "Hand Pump", "Unk…
$ State <chr> "Ekiti", "Ogun", "Ebonyi", "Enugu", "Enugu", "Be…
$ LGA <chr> "Moba", "Obafemi-Owode", "Ohaukwu", "Isi-Uzo", "…
$ Management <chr> "Unknown", "Other", "Unknown", "Unknown", "Unkno…
$ Functional <chr> "Unknown", "Functional", "Unknown", "Unknown", "…
$ Quality <chr> "Unknown", "Acceptable quality", "Unknown", "Unk…
$ geometry <POINT [°]> POINT (5.12 7.98), POINT (3.597668 6.96453…Aggregate the Data
We can use freq() of the funModeling package to display the distribution of functional field in wpdx_clean_sf. This is to help us aggregate the data as the dataset provide breakdowns of functional status. In order to only look at non functional water points, we will need to aggregate the different categories into simply functional, non functional and unknowns.
freq(data=wpdx_clean_sf, input = 'Functional')
Functional frequency percentage cumulative_perc
1 Functional 45883 48.29 48.29
2 Non-Functional 29385 30.93 79.22
3 Unknown 10656 11.22 90.44
4 Functional but needs repair 4579 4.82 95.26
5 Non-Functional due to dry season 2403 2.53 97.79
6 Functional but not in use 1686 1.77 99.56
7 Abandoned/Decommissioned 234 0.25 99.81
8 Abandoned 175 0.18 99.99
9 Non functional due to dry season 7 0.01 100.00To aggregate them into functional, non functional and unknown, we will create new data frames to store them by using the filter function
func_list = c("Functional", "Functional but needs repair", "Functional but not in use")
wpt_functional = wpdx_clean_sf %>%
filter(Functional %in% func_list)
wpt_non_functional = wpdx_clean_sf %>%
filter(!Functional %in% c(func_list, "Unknown"))
wpt_unknown = wpdx_clean_sf %>%
filter(Functional %in% "Unknown")Out of the 32, 204, records, we can gain some insights on why it might be non functional, is it due to management? Is it due to technology? Is it due to the source of the water?
Similarly, like how we aggregate functional data points, we could use freq() of the funModeling package to find out
freq(data=wpt_non_functional, input = 'Management')
Management frequency percentage cumulative_perc
1 Unknown 17617 54.70 54.70
2 Community Management 8249 25.61 80.31
3 Direct Government Operation 3831 11.90 92.21
4 Other 1941 6.03 98.24
5 School Management 397 1.23 99.47
6 Health Care Facility 128 0.40 99.87
7 Other Institutional Management 25 0.08 99.95
8 Private Operator/Delegated Management 16 0.05 100.00freq(data=wpt_non_functional, input = 'Water_Tech_Category')
Water_Tech_Category frequency percentage cumulative_perc
1 Hand Pump 20471 63.57 63.57
2 Mechanized Pump 11532 35.81 99.38
3 Unknown 169 0.52 99.90
4 Tapstand 32 0.10 100.00freq(data=wpt_non_functional, input = 'Water_Source_Category')
Water_Source_Category frequency percentage cumulative_perc
1 Well 31470 97.72 97.72
2 Spring 733 2.28 100.00
3 Piped Water 1 0.00 100.00From the results, we can conclude that
More than half of the non functional water points have an unknown management, we could ask if these water points are even managed.
Most of the non functional water points uses pumps, we could ask the question if there is an issue with the pumps and if there is a lack of expertise to repair or replace them when they fail.
97.72% of such non functional water points are made up of wells.
Combining Spatial & Aspatial Data
We can use st_intersects() to find common data points between geographical datasets. In our case we need to find the common points in the Nigeria’s LGA spatial dataset and the water point aspatial dataset
The below code does 4 things
It intersects the Nigeria LGA dataset (nga dataframe) with the water point dataset (wpdx_clean_sf dataframe) and produce a new column to denote the total number of water points in the area (Total wpt) by using
mutate()andlengths()Similar to step 1, the result of step 1 is piped to add 3 columns to denote the number of functional, non functional and unknown water points in the area to produce wpt functional, wpt non functional and wpt unknown respectively
We also add 2 new columns to find the percentage of functional and non functional water points by using
mutate()Select appropriate columns required which are the LGA area and LGA code (Column 3 & 4), Administration Level 1 Area and Administration Level 1 Code (Column 8 & 9) which represent states, the columns that were added as explained in steps 2 & 3 and the geometry multipolygon objects (Column 18 to 23) using
select()
nga_wp <- nga %>%
#combine nga with water point sf
mutate(`total wpt` = lengths(
st_intersects(nga, wpdx_clean_sf))) %>%
#add columns to produce no. of functional, non functional and unknown points
mutate(`wpt functional` = lengths(
st_intersects(nga, wpt_functional))) %>%
mutate(`wpt non functional` = lengths(
st_intersects(nga, wpt_non_functional))) %>%
mutate(`wpt unknown` = lengths(
st_intersects(nga, wpt_unknown))) %>%
#add columns to compute %
mutate(pct_functional = `wpt functional`/`total wpt`) %>%
mutate(`pct_non-functional` = `wpt non functional`/`total wpt`) %>%
select(3:4, 8:9, 18:23)Visualizing the spatial distribution of water points
We could use breaks of the summary statistics by using percentiles, this is to help us find out the distribution of water points in each quantile.
#summary(nga_wp$`total wpt`)
#summary(nga_wp$`wpt functional`)
summary(nga_wp$`wpt non functional`) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 12.25 34.00 41.60 60.75 278.00 #summary(nga_wp$`wpt unknown`)It is recommended not to use the default style with breaks as quantile since the range from the third quantile to the maximum is too wide and could result in a skewed representation. We compute the variance and standard deviation of non-functional water points first to better understand our dataset since we now need to decide which style is appropriate for the map.
var(nga_wp$`wpt non functional`)[1] 1376.914sd(nga_wp$`wpt non functional`) [1] 37.10679It appears that this dataset has a very large variance. Since the variance is so high, we would like to lower it. Using the kmeans style is one method to do this. n = 6 is choosen as after some experimentation, it appears that 6 is the optimal number of clusters.
Functions from the tmap packages is used to produce the map
First we use tm_shape() + tm_fill("ADM1_EN") to form Layer 1 of the map to form the 37 states of the map. The Pastel1 palette is used because it is difficult to read different shades of the same two to three colors; Pastel1 has more colors, making states more distinct.
Next we use tm_shape() + tm_fill("wpt non functional") to form Layer 2 of the map which are the non functional water points. The palette used in this case is Purple Red such that areas with very little water points are shaded with a very light colour.
We may switch between layers on the interactive map to superimpose the nonfunctional water locations. With so many polygons, putting it side by side can be challenging to interpret.
tm_shape(nga) +
tm_fill("ADM1_EN", palette = "Pastel1") +
tm_borders(alpha=0.5) +
tm_grid (alpha=0.2) +
tm_shape(nga_wp) +
tm_fill("wpt non functional",
palette ="PuRd", style="kmeans", n=6) +
tm_borders(alpha=0.5) +
tm_grid (alpha=0.2) +
tm_layout(main.title="non functional WP - 2 Layer map",
main.title.position="center",
main.title.size=1.2,
#legend.height = 0.35,
#legend.width = 0.35,
frame = TRUE) Using dplyr package, we can summarize find out which States has the most number non functional water points and which are the states that has the most number of LGAs by using the functions group_by, summarise and arrange
#Sum of non functional water points
nga_wp %>%
group_by(ADM1_EN) %>%
summarise(NF_Frequency = sum(`wpt non functional`),
#F_Frequency = sum(`wpt functional`),
Total_Freq = sum(`total wpt`),
NF_Ratio = (NF_Frequency / Total_Freq) * 100
) %>%
arrange(desc(NF_Frequency))Simple feature collection with 37 features and 4 fields
Geometry type: GEOMETRY
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84
# A tibble: 37 × 5
ADM1_EN NF_Frequency Total_Freq NF_Ratio geometry
<chr> <int> <int> <dbl> <GEOMETRY [°]>
1 Osun 2118 5519 38.4 POLYGON ((4.910021 7.841812, 4.…
2 Kaduna 1912 4925 38.8 POLYGON ((8.273597 11.30846, 8.…
3 Kwara 1634 3531 46.3 POLYGON ((4.876071 9.157646, 4.…
4 Kano 1570 7125 22.0 POLYGON ((8.727456 12.21461, 8.…
5 Ondo 1552 2575 60.3 POLYGON ((5.937628 7.648777, 5.…
6 Katsina 1521 5465 27.8 POLYGON ((8.3992 13.0758, 8.390…
7 Jigawa 1517 9696 15.6 POLYGON ((8.399011 12.82706, 8.…
8 Cross River 1446 3492 41.4 MULTIPOLYGON (((8.818036 5.6935…
9 Plateau 1332 4701 28.3 POLYGON ((8.820398 10.38392, 8.…
10 Oyo 1329 4085 32.5 POLYGON ((4.024729 7.664918, 4.…
# … with 27 more rows#sum of LGAs by states
nga_wp %>%
group_by(ADM1_EN) %>%
summarise(count = n())%>%
arrange(desc(count))Simple feature collection with 37 features and 2 fields
Geometry type: GEOMETRY
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84
# A tibble: 37 × 3
ADM1_EN count geometry
<chr> <int> <GEOMETRY [°]>
1 Kano 44 POLYGON ((8.727456 12.21461, 8.72491 12.21429, 8.721373 12.2…
2 Katsina 34 POLYGON ((8.3992 13.0758, 8.39043 13.08745, 8.38292 13.09174…
3 Oyo 33 POLYGON ((4.024729 7.664918, 4.039038 7.684978, 4.056281 7.6…
4 Akwa Ibom 31 MULTIPOLYGON (((7.530807 5.150259, 7.531415 5.146801, 7.5325…
5 Osun 30 POLYGON ((4.910021 7.841812, 4.911101 7.85011, 4.914135 7.85…
6 Borno 27 POLYGON ((14.58718 11.75277, 14.58861 11.75334, 14.59292 11.…
7 Imo 27 POLYGON ((7.422786 5.583626, 7.425965 5.585613, 7.426281 5.5…
8 Jigawa 27 POLYGON ((8.399011 12.82706, 8.390081 12.82528, 8.38304 12.8…
9 Delta 25 POLYGON ((5.985599 5.124185, 5.99217 5.117613, 5.995821 5.11…
10 Niger 25 POLYGON ((7.250727 10.03942, 7.240785 10.03998, 7.232374 10.…
# … with 27 more rowsObservations
According to the statistics, Osun has the highest number of non-operational water points - 2118 of them among the 37 states, followed by Kaduna (1912 water points) and Kwara (1634 water points).
Kano, despite being the State with the most number of LGAs (44), has only 1570 non functional water points (Ranked 4th) as compared to Osun that only comprises of 30 LGAs (Ranked 1st).
In contrast to Kaduna & Kwara, which are greater in size, Osun has 5519 water points, which is an interesting fact. In addition, nearly half of the water points in Kwara are not working.
Despite having a larger territory, Ondo, the state directly south-east of Osun, has over 60% of its water points that are not operational.
The south-eastern and western regions of Nigeria appear to be the hotspots for the spread of inoperative water points.
There are no non-functional water points on Nigeria’s north-eastern coast. Using the tmap package, we plot the functional map to see if there are any water points in the region or if there are none at all.
This can assist us in figuring out whether the region in the north-east is succeeding in a way that can be transferred to other parts of the nation, or whether it is uninhabited or underdeveloped.
tm_shape(nga_wp) +
tm_fill("wpt functional",
palette ="PuRd", style="kmeans", n=6) +
tm_borders(alpha=0.5) +
tm_grid (alpha=0.2) +
tm_layout(main.title="non functional WP - 2 Layer map",
main.title.position="center",
main.title.size=1.2,
#legend.height = 0.35,
#legend.width = 0.35,
frame = TRUE) The north-eastern region of Nigeria has few to no water points, which suggests to us that it may be that the region is underdeveloped or uninhabited.
Spatially Constrained Cluster Analysis
Computing Spatial Weights
We need to find the spatial weights first before we can compute global spatial correlation statistics. The spatial weights is used to define the neighbourhood relationships between the geographical units
We use poly2nb() of spdep package to compute the contiguity weight matrix. The function builds a neighbour list based on regions with contiguous boundaries. Using queen's contiguity weight matrix, we have
wm_q = poly2nb(nga_wp)
summary(wm_q)Neighbour list object:
Number of regions: 774
Number of nonzero links: 4440
Percentage nonzero weights: 0.7411414
Average number of links: 5.736434
1 region with no links:
86
Link number distribution:
0 1 2 3 4 5 6 7 8 9 10 11 12 14
1 2 14 57 125 182 140 122 72 41 12 4 1 1
2 least connected regions:
138 560 with 1 link
1 most connected region:
508 with 14 linksFrom the results, there are 774 regions in Hunan,
Using the Queen's method, 1 of them has 14 neighbours, 1 of them is an isolate (no neighbour), and 2 of them only has 1 neighbour
Building the weights matrix
After computing the spatial weights, we will need to build the weights matrix. From the result, of the neighbour list, there exist an isolated island. With this island, it is not recommended to use contiguity weight matrix as we want to find out if there is spatial correlation for non functional water points.
With that in mind, we will use distance based weight matrix instead.
By using dnearneigh() of spdep package, we can determine the distance based weight matrix. The function looks for neighbours of regions points by Euclidean distance between the lower (>=) and upper (<=) bound or with the parameter longlat = True by great circle distance in km
Building the distance based weight matrix
Fixed Weight distance matrix
The steps involved:
Using the k nearest neighbour (knn) algorithm, we can return a matrix with indices of points that belongs to the set of k nearest neighbours of each others by using
knearneigh()of spdepConvert the knn objects into a neighbours list of class nb with a list of integer vectors containing neighbour region number ids by using
knn2nb()Return the length of neighbour relationship edges by using
nbdists()of spdep. The function returns in the units of coordinates if the coordinates are projected, in km otherwise.Remove the list structure of the return objects by using
unlist()
Before we can start building the matrix, we first need to find the coordinates
The longitude is the first variable in each centroid, this enables us to obtain only the longitude.
The latitude is the second variable in each centroid, this enables us to obtain only the latitude
Using the double bracket notation [[]] and the index, we can access the latitude & longitude values.
longitude = map_dbl(nga_wp$geometry, ~st_centroid(.x)[[1]]) #longitude index 1
latitude = map_dbl(nga_wp$geometry, ~st_centroid(.x)[[2]]) #latitude index 2After getting the longitude and latitudes, we can form the coordinates object named coord using cbind.Using the head function, we can inspect the elements of coord to verify if they are correctly formatted
coord = cbind(longitude, latitude)
head(coord) longitude latitude
[1,] 7.372450 5.113107
[2,] 7.352131 5.083219
[3,] 13.322900 13.428835
[4,] 6.847325 8.825812
[5,] 7.771541 5.022061
[6,] 8.219654 6.259845Find the lower and upper bounds
k1 = knn2nb(knearneigh(coord)) #returns a list of nb objects from the result of k nearest neighbours matrix, Step 1 & 2
k1dist = unlist(nbdists(k1, coord, longlat = TRUE)) #return the length of neighbour relationship edges and remove the list structures, Step 3 & 4
summary(k1dist) Min. 1st Qu. Median Mean 3rd Qu. Max.
2.662 12.815 20.242 22.031 27.706 71.661 From the result, the largest first nearest neighbour is 71.66 km, hence by using this as the upper bound, we can be certain that all units will have at least 1 neighbour
Creating the fixed distanced weight matrix
dnearneigh will be used to compute the distance weight matrix
wm_d72 = dnearneigh(coord, 0, 72, longlat = TRUE)
wm_d72Neighbour list object:
Number of regions: 774
Number of nonzero links: 18112
Percentage nonzero weights: 3.023323
Average number of links: 23.40052 The average number of links denotes the number of non zero links divided by the number of regions. In this case, a region has about on average between 24 neighbours
We will display the structure of the weight matrix is to combine table() and card() of spdep.
The
card()function counts the neighboring regions in the neighbours list.table()creates a contingency table of the counts for each combination of factor levels using cross-classifying factors.
table(nga_wp$ADM2_EN, card(wm_d72))
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Aba North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aba South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abadam 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abaji 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Abak 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abakaliki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Abeokuta North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Abeokuta South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aboh-Mbaise 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abua/Odual 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Abuja Municipal 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Adavi 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ado 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado-Odo/Ota 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado Ekiti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Afijio 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Afikpo North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Afikpo South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agaie 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agatu 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Agege 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aguata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agwara 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahiazu-Mbaise 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahoada East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahoada West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyedade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyedire 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyekire (Gbonyin) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ajaokuta 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ajeromi-Ifelodun 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ajingi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akamkpa 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Akinyele 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akko 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko-Edo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Akoko North East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko North West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko South East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Akoko South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Akpabuyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Akuku Toru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Akure North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akure South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akwanga 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Albasu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aleiro 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Alimosho 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Alkaleri 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Amuwo-Odofin 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anambra East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anambra West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anaocha 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Andoni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aninri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aniocha North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aniocha South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anka 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ankpa 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Apa 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Apapa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ardo-Kola 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Arewa-Dandi 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Argungu 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Arochukwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Asa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Asari-Toru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Askira/Uba 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atakumosa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atakumosa West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atiba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Atigbo 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Augie 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Auyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Awe 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Awgu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awka North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awka South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ayamelum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Babura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Badagry 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Bade 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bagudo 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bagwai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bakassi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Bakori 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Bakura 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Balanga 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Bali 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bama 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Barikin Ladi 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Baruten 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bassa 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Batagarawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Batsari 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Bauchi 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Baure 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Bayo 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Bebeji 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bekwara 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Bende 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Biase 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bichi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bida 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Billiri 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bindawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Binji 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Biriniwa 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Birni Kudu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Birnin-Gwari 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Birnin Kebbi 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Birnin Magaji 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Biu 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bodinga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Bogoro 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Boki 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bokkos 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Boluwaduro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bomadi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Bonny 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Borgu 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Boripe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bosso 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Brass 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Buji 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Bukkuyum 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bungudu 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bunkure 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bunza 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bursari 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Buruku 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Burutu 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Bwari 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Calabar-Municipal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Calabar South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chanchaga 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Charanchi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chibok 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Chikun 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dala 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Damaturu 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Damban 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dambatta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Damboa 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dan Musa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Dandi 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dandume 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Dange-Shuni 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Danja 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Darazo 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dass 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Daura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Dawakin Kudu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dawakin Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Degema 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dekina 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Demsa 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Dikwa 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Doguwa 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Doma 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Donga 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dukku 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dunukofia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dutse 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Dutsi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dutsin-Ma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Eastern Obolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ebonyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Edati 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ede North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ede South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Edu 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Efon 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Egbado North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Egbado South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Egbeda 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Egbedore 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Egor 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ehime-Mbano 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ejigbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekeremor 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Eket 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekiti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekiti East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekiti South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekiti West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ekwusigo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Eleme 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Emohua 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Emure 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Enugu East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Enugu North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Enugu South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Epe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Esan Central 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Esan North East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Esan South East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Esan West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Ese-Odo 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Esit - Eket 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Essien Udim 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Etche 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ethiope East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Ethiope West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Eti-Osa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Etim Ekpo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Etinan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Etsako Central 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Etsako East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Etsako West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Etung 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ewekoro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ezeagu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ezinihitte 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ezza North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ezza South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fagge 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fakai 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Faskari 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Fika 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Fufore 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Funakaye 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Fune 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Funtua 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Gabasawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gada 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gagarawa 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Gamawa 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Ganjuwa 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ganye 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Garki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Garko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Garum Mallam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gashaka 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gassol 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gaya 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gbako 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Gboko 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Geidam 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gezawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Giade 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Girei 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Giwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Gokana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gombe 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Gombi 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Goronyo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Gubio 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gudu 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gujba 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gulani 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Guma 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Gumel 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Gummi 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gurara 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Guri 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Gusau 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Guyuk 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Guzamala 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gwadabawa 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Gwagwalada 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Gwale 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gwandu 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Gwaram 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Gwarzo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Gwer East 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Gwer West 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Gwiwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Ogori/Magongo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Ogu/Bolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogun waterside 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Oguta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohafia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohaji/Egbema 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohaozara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohaukwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohimini 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Oji-River 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ojo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oju 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Oke-Ero 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okehi 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Okene 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Okigwe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okitipupa 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Okobo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okpe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Okpokwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Okrika 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ola-oluwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Olamabolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Olorunda 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Olorunsogo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Oluyole 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Omala 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Omumma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ona-Ara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ondo East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ondo West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onicha 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onitsha North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onitsha South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onna 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Opobo/Nkoro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oredo 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Orelope 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orhionmwon 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ori Ire 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Oriade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orlu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orolu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oron 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orsu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oru East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oru West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oruk Anam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orumba North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orumba South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ose 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Oshimili North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oshimili South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oshodi-Isolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Osisioma Ngwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Osogbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oturkpo 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ovia North East 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ovia South West 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Owan East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Owan West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Owerri-Municipal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owerri North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owerri West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Oye 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyigbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyo East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyo West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyun 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Paikoro 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pankshin 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Patani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Pategi 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Port-Harcourt 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Potiskum 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Qua'an Pan 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Rabah 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Rafi 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rano 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Remo North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rijau 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rimi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Rimin Gado 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ringim 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Riyom 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Rogo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Roni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sabon-Gari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Sabon Birni 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sabuwa 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Safana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Sagbama 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Sakaba 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Saki East 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Saki West 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sandamu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Sanga 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Sapele 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Sardauna 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shagamu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shagari 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Shanga 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shani 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Shanono 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shelleng 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Shendam 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shinkafi 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shira 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Shiroro 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomgom 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomolu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Silame 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Soba 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Sokoto North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Sokoto South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Song 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Southern Ijaw 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Sule-Tankarkar 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Suleja 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Sumaila 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Suru 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Surulere 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tafa 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Tafawa-Balewa 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Takai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Takum 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Talata Mafara 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tambuwal 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Tangaza 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Tarauni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarka 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Tarmua 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Taura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toro 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toto 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toungo 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tsafe 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Tsanyawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tudun Wada 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Tureta 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Udenu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Udi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Udung Uko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ughelli North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ughelli South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Ugwunagbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uhunmwonde 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Ukanafun 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukum 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwa West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwuani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umu-Nneochi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ungogo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Unuimo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uruan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Urue-Offong/Oruko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ushongo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Ussa 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uvwie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Uyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uzo-Uwani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Vandeikya 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Wamako 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Wamba 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Warawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warji 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warri North 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Warri South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Warri South West 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Wasagu/Danko 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wase 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wudil 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wukari 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wurno 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Wushishi 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yabo 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Yagba East 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Yagba West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Yakurr 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Yala 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Yamaltu/Deba 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Yankwashi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Yauri 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yenegoa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Yola North 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yola South 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Yorro 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yunusari 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yusufari 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zaki 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Zango 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Zango-Kataf 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Zaria 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Zing 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Zurmi 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zuru 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Aba North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aba South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abadam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abaji 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abak 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abakaliki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abeokuta North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abeokuta South 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Abi 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aboh-Mbaise 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abua/Odual 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Abuja Municipal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Adavi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado-Odo/Ota 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ado Ekiti 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Afijio 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Afikpo North 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Afikpo South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Agaie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agatu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agege 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Aguata 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Agwara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahiazu-Mbaise 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ahoada East 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Ahoada West 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyedade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyedire 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aiyekire (Gbonyin) 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Ajaokuta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ajeromi-Ifelodun 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Ajingi 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Akamkpa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akinyele 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Akko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko-Edo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko North East 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko North West 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko South East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akoko South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akpabuyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akuku Toru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akure North 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akure South 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Akwanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Albasu 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aleiro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Alimosho 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Alkaleri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Amuwo-Odofin 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anambra East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Anambra West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Anaocha 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Andoni 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Aninri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Aniocha North 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Aniocha South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Anka 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ankpa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Apa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Apapa 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Ardo-Kola 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Arewa-Dandi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Argungu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Arochukwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Asa 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Asari-Toru 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Askira/Uba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atakumosa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atakumosa West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atiba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Atigbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Augie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Auyo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awgu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awka North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Awka South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ayamelum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Babura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Badagry 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bagudo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bagwai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Bakassi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bakori 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bakura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Balanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bali 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bama 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Barikin Ladi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Baruten 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bassa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Batagarawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Batsari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bauchi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Baure 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bayo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bebeji 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Bekwara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Mbaitoli 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Misau 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Monguno 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Moro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Mubi North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Musawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Nafada 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Nasarawa-Eggon 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ndokwa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Nembe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ngala 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Ngaski 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ngor-Okpala 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Nguru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ningi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Njaba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Njikoka 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Nkanu East 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Nkanu West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Nkwerre 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Nnewi North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Nsit Atai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
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Nsukka 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Numan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Nwangele 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obafemi-Owode 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obanliku 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obi Ngwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obia/Akpor 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Obokun 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obot Akara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obowo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obubra 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Obudu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Odeda 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Odigbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Odo-Otin 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Odogbolu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Odukpani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Offa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Ofu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogba/Egbema/Ndoni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Ogbadibo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogbaru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogbia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogbomosho North 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Ogbomosho South 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Ogo Oluwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Ogoja 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogori/Magongo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ogu/Bolo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Ogun waterside 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oguta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohafia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Ohaji/Egbema 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohaozara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Ohaukwu 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ohimini 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oji-River 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ojo 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oju 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oke-Ero 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okehi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okene 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okigwe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okitipupa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okobo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Okpe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okpokwu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Okrika 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Ola-oluwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Olamabolo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Olorunda 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Olorunsogo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oluyole 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Omala 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Omumma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ona-Ara 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Ondo East 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Ondo West 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onicha 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Onitsha North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onitsha South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Onna 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Opobo/Nkoro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Oredo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orelope 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orhionmwon 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ori Ire 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oriade 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orlu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orolu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oron 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Orsu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oru East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oru West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oruk Anam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orumba North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Orumba South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ose 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oshimili North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Oshimili South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oshodi-Isolo 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Osisioma Ngwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Osogbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oturkpo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ovia North East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ovia South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owan East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owan West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owerri-Municipal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owerri North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owerri West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Owo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oye 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Oyi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyigbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyo East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Oyo West 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Oyun 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Paikoro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pankshin 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Patani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pategi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Port-Harcourt 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Potiskum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Qua'an Pan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rabah 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rafi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rano 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Remo North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rijau 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rimi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rimin Gado 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Ringim 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Riyom 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Rogo 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Roni 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Sabon-Gari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sabon Birni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sabuwa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Safana 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sagbama 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sakaba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Saki East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Saki West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sandamu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sapele 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sardauna 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shagamu 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
Shagari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shanono 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Shelleng 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shendam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shinkafi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shira 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shiroro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomgom 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomolu 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Silame 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Soba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sokoto North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sokoto South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Song 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Southern Ijaw 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sule-Tankarkar 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Suleja 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sumaila 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Suru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Surulere 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0
Tafa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tafawa-Balewa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Takai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Takum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Talata Mafara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tambuwal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tangaza 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarauni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Tarka 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarmua 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
Toro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toto 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toungo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tsafe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tsanyawa 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Tudun Wada 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tureta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udenu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Udu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udung Uko 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Ukum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwa West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Umu-Nneochi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Urue-Offong/Oruko 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Ushongo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Zango 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zango-Kataf 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zaria 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Zuru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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Dawakin Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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Shagamu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shagari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shanga 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shanono 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shelleng 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shendam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shinkafi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shira 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shiroro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomgom 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Shomolu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Silame 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Soba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sokoto North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sokoto South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Song 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Southern Ijaw 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sule-Tankarkar 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Suleja 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Sumaila 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Suru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Surulere 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tafa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tafawa-Balewa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Takai 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Takum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Talata Mafara 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tambuwal 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tangaza 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarauni 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarka 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tarmua 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Taura 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tofa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toto 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Toungo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tsafe 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tsanyawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tudun Wada 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Tureta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udenu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udu 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Udung Uko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ughelli North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ughelli South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ugwunagbo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Uhunmwonde 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukanafun 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Ukum 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ukwa East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Ukwa West 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Ukwuani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umu-Nneochi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Umuahia South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ungogo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Unuimo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uruan 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Urue-Offong/Oruko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ushongo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Ussa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uvwie 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Uyo 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Uzo-Uwani 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Vandeikya 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wamako 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wamba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warawa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warji 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warri North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warri South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Warri South West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wasagu/Danko 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wase 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wudil 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wukari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wurno 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wushishi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yabo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yagba East 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yagba West 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yakurr 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yala 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yamaltu/Deba 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yankwashi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yauri 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yenegoa 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yola North 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yola South 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yorro 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yunusari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yusufari 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zaki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zango 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zango-Kataf 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zaria 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zurmi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Zuru 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
61 62 63 64 65 67 68 70
Aba North 0 0 1 0 0 0 0 0
Aba South 0 1 0 0 0 0 0 0
Abadam 0 0 0 0 0 0 0 0
Abaji 0 0 0 0 0 0 0 0
Abak 0 0 0 0 0 0 0 0
Abakaliki 0 0 0 0 0 0 0 0
Abeokuta North 0 0 0 0 0 0 0 0
Abeokuta South 0 0 0 0 0 0 0 0
Abi 0 0 0 0 0 0 0 0
Aboh-Mbaise 0 0 1 0 0 0 0 0
Abua/Odual 0 0 0 0 0 0 0 0
Abuja Municipal 0 0 0 0 0 0 0 0
Adavi 0 0 0 0 0 0 0 0
Ado 0 0 0 0 0 0 0 0
Ado-Odo/Ota 0 0 0 0 0 0 0 0
Ado Ekiti 0 0 0 0 0 0 0 0
Afijio 0 0 0 0 0 0 0 0
Afikpo North 0 0 0 0 0 0 0 0
Afikpo South 0 0 0 0 0 0 0 0
Agaie 0 0 0 0 0 0 0 0
Agatu 0 0 0 0 0 0 0 0
Agege 0 0 0 0 0 0 0 0
Aguata 0 0 0 1 0 0 0 0
Agwara 0 0 0 0 0 0 0 0
Ahiazu-Mbaise 0 0 0 0 0 0 1 0
Ahoada East 0 0 0 0 0 0 0 0
Ahoada West 0 0 0 0 0 0 0 0
Aiyedade 0 0 0 0 0 0 0 0
Aiyedire 0 0 0 0 0 0 0 0
Aiyekire (Gbonyin) 0 0 0 0 0 0 0 0
Ajaokuta 0 0 0 0 0 0 0 0
Ajeromi-Ifelodun 0 0 0 0 0 0 0 0
Ajingi 0 0 0 0 0 0 0 0
Akamkpa 0 0 0 0 0 0 0 0
Akinyele 0 0 0 0 0 0 0 0
Akko 0 0 0 0 0 0 0 0
Akoko-Edo 0 0 0 0 0 0 0 0
Akoko North East 0 0 0 0 0 0 0 0
Akoko North West 0 0 0 0 0 0 0 0
Akoko South East 0 0 0 0 0 0 0 0
Akoko South West 0 0 0 0 0 0 0 0
Akpabuyo 0 0 0 0 0 0 0 0
Akuku Toru 0 0 0 0 0 0 0 0
Akure North 0 0 0 0 0 0 0 0
Akure South 0 0 0 0 0 0 0 0
Akwanga 0 0 0 0 0 0 0 0
Albasu 0 0 0 0 0 0 0 0
Aleiro 0 0 0 0 0 0 0 0
Alimosho 0 0 0 0 0 0 0 0
Alkaleri 0 0 0 0 0 0 0 0
Amuwo-Odofin 0 0 0 0 0 0 0 0
Anambra East 0 0 0 0 0 0 0 0
Anambra West 0 0 0 0 0 0 0 0
Anaocha 0 0 0 0 0 0 0 0
Andoni 0 0 0 0 0 0 0 0
Aninri 0 0 0 0 0 0 0 0
Aniocha North 0 0 0 0 0 0 0 0
Aniocha South 0 0 0 0 0 0 0 0
Anka 0 0 0 0 0 0 0 0
Ankpa 0 0 0 0 0 0 0 0
Apa 0 0 0 0 0 0 0 0
Apapa 0 0 0 0 0 0 0 0
Ardo-Kola 0 0 0 0 0 0 0 0
Arewa-Dandi 0 0 0 0 0 0 0 0
Argungu 0 0 0 0 0 0 0 0
Arochukwu 0 0 0 0 0 0 0 0
Asa 0 0 0 0 0 0 0 0
Asari-Toru 0 0 0 0 0 0 0 0
Askira/Uba 0 0 0 0 0 0 0 0
Atakumosa East 0 0 0 0 0 0 0 0
Atakumosa West 0 0 0 0 0 0 0 0
Atiba 0 0 0 0 0 0 0 0
Atigbo 0 0 0 0 0 0 0 0
Augie 0 0 0 0 0 0 0 0
Auyo 0 0 0 0 0 0 0 0
Awe 0 0 0 0 0 0 0 0
Awgu 0 0 0 0 0 0 0 0
Awka North 0 0 0 0 0 0 0 0
Awka South 0 0 0 0 0 0 0 0
Ayamelum 0 0 0 0 0 0 0 0
Babura 0 0 0 0 0 0 0 0
Badagry 0 0 0 0 0 0 0 0
Bade 0 0 0 0 0 0 0 0
Bagudo 0 0 0 0 0 0 0 0
Bagwai 0 0 0 0 0 0 0 0
Bakassi 0 0 0 0 0 0 0 0
Bakori 0 0 0 0 0 0 0 0
Bakura 0 0 0 0 0 0 0 0
Balanga 0 0 0 0 0 0 0 0
Bali 0 0 0 0 0 0 0 0
Bama 0 0 0 0 0 0 0 0
Barikin Ladi 0 0 0 0 0 0 0 0
Baruten 0 0 0 0 0 0 0 0
Bassa 0 0 0 0 0 0 0 0
Batagarawa 0 0 0 0 0 0 0 0
Batsari 0 0 0 0 0 0 0 0
Bauchi 0 0 0 0 0 0 0 0
Baure 0 0 0 0 0 0 0 0
Bayo 0 0 0 0 0 0 0 0
Bebeji 0 0 0 0 0 0 0 0
Bekwara 0 0 0 0 0 0 0 0
Bende 0 0 0 0 0 0 0 0
Biase 0 0 0 0 0 0 0 0
Bichi 0 0 0 0 0 0 0 0
Bida 0 0 0 0 0 0 0 0
Billiri 0 0 0 0 0 0 0 0
Bindawa 0 0 0 0 0 0 0 0
Binji 0 0 0 0 0 0 0 0
Biriniwa 0 0 0 0 0 0 0 0
Birni Kudu 0 0 0 0 0 0 0 0
Birnin-Gwari 0 0 0 0 0 0 0 0
Birnin Kebbi 0 0 0 0 0 0 0 0
Birnin Magaji 0 0 0 0 0 0 0 0
Biu 0 0 0 0 0 0 0 0
Bodinga 0 0 0 0 0 0 0 0
Bogoro 0 0 0 0 0 0 0 0
Boki 0 0 0 0 0 0 0 0
Bokkos 0 0 0 0 0 0 0 0
Boluwaduro 0 0 0 0 0 0 0 0
Bomadi 0 0 0 0 0 0 0 0
Bonny 0 0 0 0 0 0 0 0
Borgu 0 0 0 0 0 0 0 0
Boripe 0 0 0 0 0 0 0 0
Bosso 0 0 0 0 0 0 0 0
Brass 0 0 0 0 0 0 0 0
Buji 0 0 0 0 0 0 0 0
Bukkuyum 0 0 0 0 0 0 0 0
Bungudu 0 0 0 0 0 0 0 0
Bunkure 0 0 0 0 0 0 0 0
Bunza 0 0 0 0 0 0 0 0
Bursari 0 0 0 0 0 0 0 0
Buruku 0 0 0 0 0 0 0 0
Burutu 0 0 0 0 0 0 0 0
Bwari 0 0 0 0 0 0 0 0
Calabar-Municipal 0 0 0 0 0 0 0 0
Calabar South 0 0 0 0 0 0 0 0
Chanchaga 0 0 0 0 0 0 0 0
Charanchi 0 0 0 0 0 0 0 0
Chibok 0 0 0 0 0 0 0 0
Chikun 0 0 0 0 0 0 0 0
Dala 0 0 0 0 0 0 0 0
Damaturu 0 0 0 0 0 0 0 0
Damban 0 0 0 0 0 0 0 0
Dambatta 0 0 0 0 0 0 0 0
Damboa 0 0 0 0 0 0 0 0
Dan Musa 0 0 0 0 0 0 0 0
Dandi 0 0 0 0 0 0 0 0
Dandume 0 0 0 0 0 0 0 0
Dange-Shuni 0 0 0 0 0 0 0 0
Danja 0 0 0 0 0 0 0 0
Darazo 0 0 0 0 0 0 0 0
Dass 0 0 0 0 0 0 0 0
Daura 0 0 0 0 0 0 0 0
Dawakin Kudu 0 0 0 0 0 0 0 0
Dawakin Tofa 0 0 0 0 0 0 0 0
Degema 0 0 0 0 0 0 0 0
Dekina 0 0 0 0 0 0 0 0
Demsa 0 0 0 0 0 0 0 0
Dikwa 0 0 0 0 0 0 0 0
Doguwa 0 0 0 0 0 0 0 0
Doma 0 0 0 0 0 0 0 0
Donga 0 0 0 0 0 0 0 0
Dukku 0 0 0 0 0 0 0 0
Dunukofia 0 0 0 0 0 0 0 0
Dutse 0 0 0 0 0 0 0 0
Dutsi 0 0 0 0 0 0 0 0
Dutsin-Ma 0 0 0 0 0 0 0 0
Eastern Obolo 0 0 0 0 0 0 0 0
Ebonyi 0 0 0 0 0 0 0 0
Edati 0 0 0 0 0 0 0 0
Ede North 0 0 0 0 0 0 0 0
Ede South 0 0 0 0 0 0 0 0
Edu 0 0 0 0 0 0 0 0
Efon 0 0 0 0 0 0 0 0
Egbado North 0 0 0 0 0 0 0 0
Egbado South 0 0 0 0 0 0 0 0
Egbeda 0 0 0 0 0 0 0 0
Egbedore 0 0 0 0 0 0 0 0
Egor 0 0 0 0 0 0 0 0
Ehime-Mbano 0 0 0 0 1 0 0 0
Ejigbo 0 0 0 0 0 0 0 0
Ekeremor 0 0 0 0 0 0 0 0
Eket 0 0 0 0 0 0 0 0
Ekiti 0 0 0 0 0 0 0 0
Ekiti East 0 0 0 0 0 0 0 0
Ekiti South West 0 0 0 0 0 0 0 0
Ekiti West 0 0 0 0 0 0 0 0
Ekwusigo 0 0 0 0 0 0 0 0
Eleme 0 0 0 0 0 0 0 0
Emohua 0 0 0 0 0 0 0 0
Emure 0 0 0 0 0 0 0 0
Enugu East 0 0 0 0 0 0 0 0
Enugu North 0 0 0 0 0 0 0 0
Enugu South 0 0 0 0 0 0 0 0
Epe 0 0 0 0 0 0 0 0
Esan Central 0 0 0 0 0 0 0 0
Esan North East 0 0 0 0 0 0 0 0
Esan South East 0 0 0 0 0 0 0 0
Esan West 0 0 0 0 0 0 0 0
Ese-Odo 0 0 0 0 0 0 0 0
Esit - Eket 0 0 0 0 0 0 0 0
Essien Udim 0 0 0 0 0 0 0 0
Etche 0 0 0 0 0 0 0 0
Ethiope East 0 0 0 0 0 0 0 0
Ethiope West 0 0 0 0 0 0 0 0
Eti-Osa 0 0 0 0 0 0 0 0
Etim Ekpo 0 0 0 0 0 0 0 0
Etinan 0 0 0 0 0 0 0 0
Etsako Central 0 0 0 0 0 0 0 0
Etsako East 0 0 0 0 0 0 0 0
Etsako West 0 0 0 0 0 0 0 0
Etung 0 0 0 0 0 0 0 0
Ewekoro 0 0 0 0 0 0 0 0
Ezeagu 0 0 0 0 0 0 0 0
Ezinihitte 0 0 0 1 0 0 0 0
Ezza North 0 0 0 0 0 0 0 0
Ezza South 0 0 0 0 0 0 0 0
Fagge 0 0 0 0 0 0 0 0
Fakai 0 0 0 0 0 0 0 0
Faskari 0 0 0 0 0 0 0 0
Fika 0 0 0 0 0 0 0 0
Fufore 0 0 0 0 0 0 0 0
Funakaye 0 0 0 0 0 0 0 0
Fune 0 0 0 0 0 0 0 0
Funtua 0 0 0 0 0 0 0 0
Gabasawa 0 0 0 0 0 0 0 0
Gada 0 0 0 0 0 0 0 0
Gagarawa 0 0 0 0 0 0 0 0
Gamawa 0 0 0 0 0 0 0 0
Ganjuwa 0 0 0 0 0 0 0 0
Ganye 0 0 0 0 0 0 0 0
Garki 0 0 0 0 0 0 0 0
Garko 0 0 0 0 0 0 0 0
Garum Mallam 0 0 0 0 0 0 0 0
Gashaka 0 0 0 0 0 0 0 0
Gassol 0 0 0 0 0 0 0 0
Gaya 0 0 0 0 0 0 0 0
Gbako 0 0 0 0 0 0 0 0
Gboko 0 0 0 0 0 0 0 0
Geidam 0 0 0 0 0 0 0 0
Gezawa 0 0 0 0 0 0 0 0
Giade 0 0 0 0 0 0 0 0
Girei 0 0 0 0 0 0 0 0
Giwa 0 0 0 0 0 0 0 0
Gokana 0 0 0 0 0 0 0 0
Gombe 0 0 0 0 0 0 0 0
Gombi 0 0 0 0 0 0 0 0
Goronyo 0 0 0 0 0 0 0 0
Gubio 0 0 0 0 0 0 0 0
Gudu 0 0 0 0 0 0 0 0
Gujba 0 0 0 0 0 0 0 0
Gulani 0 0 0 0 0 0 0 0
Guma 0 0 0 0 0 0 0 0
Gumel 0 0 0 0 0 0 0 0
Gummi 0 0 0 0 0 0 0 0
Gurara 0 0 0 0 0 0 0 0
Guri 0 0 0 0 0 0 0 0
Gusau 0 0 0 0 0 0 0 0
Guyuk 0 0 0 0 0 0 0 0
Guzamala 0 0 0 0 0 0 0 0
Gwadabawa 0 0 0 0 0 0 0 0
Gwagwalada 0 0 0 0 0 0 0 0
Gwale 0 0 0 0 0 0 0 0
Gwandu 0 0 0 0 0 0 0 0
Gwaram 0 0 0 0 0 0 0 0
Gwarzo 0 0 0 0 0 0 0 0
Gwer East 0 0 0 0 0 0 0 0
Gwer West 0 0 0 0 0 0 0 0
Gwiwa 0 0 0 0 0 0 0 0
Gwoza 0 0 0 0 0 0 0 0
Hadejia 0 0 0 0 0 0 0 0
Hawul 0 0 0 0 0 0 0 0
Hong 0 0 0 0 0 0 0 0
Ibadan North 0 0 0 0 0 0 0 0
Ibadan North East 0 0 0 0 0 0 0 0
Ibadan North West 0 0 0 0 0 0 0 0
Ibadan South East 0 0 0 0 0 0 0 0
Ibadan South West 0 0 0 0 0 0 0 0
Ibaji 0 0 0 0 0 0 0 0
Ibarapa Central 0 0 0 0 0 0 0 0
Ibarapa East 0 0 0 0 0 0 0 0
Ibarapa North 0 0 0 0 0 0 0 0
Ibeju/Lekki 0 0 0 0 0 0 0 0
Ibeno 0 0 0 0 0 0 0 0
Ibesikpo Asutan 0 0 0 0 0 0 0 0
Ibi 0 0 0 0 0 0 0 0
Ibiono Ibom 0 0 0 0 0 0 0 0
Idah 0 0 0 0 0 0 0 0
Idanre 0 0 0 0 0 0 0 0
Ideato North 0 0 0 0 0 0 0 0
Ideato South 1 0 0 0 0 0 0 0
Idemili North 0 0 0 0 0 0 0 0
Idemili South 0 0 0 0 0 0 0 0
Ido 0 0 0 0 0 0 0 0
Ido-Osi 0 0 0 0 0 0 0 0
Ifako-Ijaye 0 0 0 0 0 0 0 0
Ife Central 0 0 0 0 0 0 0 0
Ife East 0 0 0 0 0 0 0 0
Ife North 0 0 0 0 0 0 0 0
Ife South 0 0 0 0 0 0 0 0
Ifedayo 0 0 0 0 0 0 0 0
Ifedore 0 0 0 0 0 0 0 0
Ifelodun 0 0 0 0 0 0 0 0
Ifo 0 0 0 0 0 0 0 0
Igabi 0 0 0 0 0 0 0 0
Igalamela-Odolu 0 0 0 0 0 0 0 0
Igbo-Etiti 0 0 0 0 0 0 0 0
Igbo-Eze North 0 0 0 0 0 0 0 0
Igbo-Eze South 0 0 0 0 0 0 0 0
Igueben 0 0 0 0 0 0 0 0
Ihiala 0 0 0 0 0 0 0 0
Ihitte/Uboma 0 0 0 0 1 0 0 0
Ijebu East 0 0 0 0 0 0 0 0
Ijebu North 0 0 0 0 0 0 0 0
Ijebu North East 0 0 0 0 0 0 0 0
Ijebu Ode 0 0 0 0 0 0 0 0
Ijero 0 0 0 0 0 0 0 0
Ijumu 0 0 0 0 0 0 0 0
Ika 0 0 0 0 0 0 0 0
Ika North East 0 0 0 0 0 0 0 0
Ika South 0 0 0 0 0 0 0 0
Ikara 0 0 0 0 0 0 0 0
Ikeduru 1 0 0 0 0 0 0 0
Ikeja 0 0 0 0 0 0 0 0
Ikenne 0 0 0 0 0 0 0 0
Ikere 0 0 0 0 0 0 0 0
Ikole 0 0 0 0 0 0 0 0
Ikom 0 0 0 0 0 0 0 0
Ikono 0 0 0 0 0 0 0 0
Ikorodu 0 0 0 0 0 0 0 0
Ikot Abasi 0 0 0 0 0 0 0 0
Ikot Ekpene 0 0 0 0 0 0 0 0
Ikpoba-Okha 0 0 0 0 0 0 0 0
Ikwerre 0 0 0 0 0 0 0 0
Ikwo 0 0 0 0 0 0 0 0
Ikwuano 0 1 0 0 0 0 0 0
Ila 0 0 0 0 0 0 0 0
Ilaje 0 0 0 0 0 0 0 0
Ile-Oluji-Okeigbo 0 0 0 0 0 0 0 0
Ilejemeji 0 0 0 0 0 0 0 0
Ilesha East 0 0 0 0 0 0 0 0
Ilesha West 0 0 0 0 0 0 0 0
Illela 0 0 0 0 0 0 0 0
Ilorin East 0 0 0 0 0 0 0 0
Ilorin South 0 0 0 0 0 0 0 0
Ilorin West 0 0 0 0 0 0 0 0
Imeko-Afon 0 0 0 0 0 0 0 0
Ingawa 0 0 0 0 0 0 0 0
Ini 0 0 0 0 0 0 0 0
Ipokia 0 0 0 0 0 0 0 0
Irele 0 0 0 0 0 0 0 0
Irepo 0 0 0 0 0 0 0 0
Irepodun 0 0 0 0 0 0 0 0
Irepodun/Ifelodun 0 0 0 0 0 0 0 0
Irewole 0 0 0 0 0 0 0 0
Isa 0 0 0 0 0 0 0 0
Ise/Orun 0 0 0 0 0 0 0 0
Iseyin 0 0 0 0 0 0 0 0
Ishielu 0 0 0 0 0 0 0 0
Isi-Uzo 0 0 0 0 0 0 0 0
Isiala-Ngwa North 0 0 1 0 0 0 0 0
Isiala-Ngwa South 0 0 1 0 0 0 0 0
Isiala Mbano 0 0 0 0 0 1 0 0
Isin 0 0 0 0 0 0 0 0
Isiukwuato 0 0 1 0 0 0 0 0
Isokan 0 0 0 0 0 0 0 0
Isoko North 0 0 0 0 0 0 0 0
Isoko South 0 0 0 0 0 0 0 0
Isu 0 1 0 0 0 0 0 0
Itas/Gadau 0 0 0 0 0 0 0 0
Itesiwaju 0 0 0 0 0 0 0 0
Itu 0 0 0 0 0 0 0 0
Ivo 0 0 0 0 0 0 0 0
Iwajowa 0 0 0 0 0 0 0 0
Iwo 0 0 0 0 0 0 0 0
Izzi 0 0 0 0 0 0 0 0
Jaba 0 0 0 0 0 0 0 0
Jada 0 0 0 0 0 0 0 0
Jahun 0 0 0 0 0 0 0 0
Jakusko 0 0 0 0 0 0 0 0
Jalingo 0 0 0 0 0 0 0 0
Jama'are 0 0 0 0 0 0 0 0
Jega 0 0 0 0 0 0 0 0
Jema'a 0 0 0 0 0 0 0 0
Jere 0 0 0 0 0 0 0 0
Jibia 0 0 0 0 0 0 0 0
Jos East 0 0 0 0 0 0 0 0
Jos North 0 0 0 0 0 0 0 0
Jos South 0 0 0 0 0 0 0 0
Kabba/Bunu 0 0 0 0 0 0 0 0
Kabo 0 0 0 0 0 0 0 0
Kachia 0 0 0 0 0 0 0 0
Kaduna North 0 0 0 0 0 0 0 0
Kaduna South 0 0 0 0 0 0 0 0
Kafin Hausa 0 0 0 0 0 0 0 0
Kafur 0 0 0 0 0 0 0 0
Kaga 0 0 0 0 0 0 0 0
Kagarko 0 0 0 0 0 0 0 0
Kaiama 0 0 0 0 0 0 0 0
Kaita 0 0 0 0 0 0 0 0
Kajola 0 0 0 0 0 0 0 0
Kajuru 0 0 0 0 0 0 0 0
Kala/Balge 0 0 0 0 0 0 0 0
Kalgo 0 0 0 0 0 0 0 0
Kaltungo 0 0 0 0 0 0 0 0
Kanam 0 0 0 0 0 0 0 0
Kankara 0 0 0 0 0 0 0 0
Kanke 0 0 0 0 0 0 0 0
Kankia 0 0 0 0 0 0 0 0
Kano Municipal 0 0 0 0 0 0 0 0
Karasuwa 0 0 0 0 0 0 0 0
Karaye 0 0 0 0 0 0 0 0
Karim-Lamido 0 0 0 0 0 0 0 0
Karu 0 0 0 0 0 0 0 0
Katagum 0 0 0 0 0 0 0 0
Katcha 0 0 0 0 0 0 0 0
Katsina 0 0 0 0 0 0 0 0
Katsina-Ala 0 0 0 0 0 0 0 0
Kaugama 0 0 0 0 0 0 0 0
Kaura 0 0 0 0 0 0 0 0
Kaura Namoda 0 0 0 0 0 0 0 0
Kauru 0 0 0 0 0 0 0 0
Kazaure 0 0 0 0 0 0 0 0
Keana 0 0 0 0 0 0 0 0
Kebbe 0 0 0 0 0 0 0 0
Keffi 0 0 0 0 0 0 0 0
Khana 0 0 0 0 0 0 0 0
Kibiya 0 0 0 0 0 0 0 0
Kirfi 0 0 0 0 0 0 0 0
Kiri Kasamma 0 0 0 0 0 0 0 0
Kiru 0 0 0 0 0 0 0 0
Kiyawa 0 0 0 0 0 0 0 0
Kogi 0 0 0 0 0 0 0 0
Koko/Besse 0 0 0 0 0 0 0 0
Kokona 0 0 0 0 0 0 0 0
Kolokuma/Opokuma 0 0 0 0 0 0 0 0
Konduga 0 0 0 0 0 0 0 0
Konshisha 0 0 0 0 0 0 0 0
Kontagora 0 0 0 0 0 0 0 0
Kosofe 0 0 0 0 0 0 0 0
Kubau 0 0 0 0 0 0 0 0
Kudan 0 0 0 0 0 0 0 0
Kuje 0 0 0 0 0 0 0 0
Kukawa 0 0 0 0 0 0 0 0
Kumbotso 0 0 0 0 0 0 0 0
Kunchi 0 0 0 0 0 0 0 0
Kura 0 0 0 0 0 0 0 0
Kurfi 0 0 0 0 0 0 0 0
Kurmi 0 0 0 0 0 0 0 0
Kusada 0 0 0 0 0 0 0 0
Kwali 0 0 0 0 0 0 0 0
Kwami 0 0 0 0 0 0 0 0
Kwande 0 0 0 0 0 0 0 0
Kware 0 0 0 0 0 0 0 0
Kwaya Kusar 0 0 0 0 0 0 0 0
Lafia 0 0 0 0 0 0 0 0
Lagelu 0 0 0 0 0 0 0 0
Lagos Island 0 0 0 0 0 0 0 0
Lagos Mainland 0 0 0 0 0 0 0 0
Lamurde 0 0 0 0 0 0 0 0
Langtang North 0 0 0 0 0 0 0 0
Langtang South 0 0 0 0 0 0 0 0
Lapai 0 0 0 0 0 0 0 0
Lau 0 0 0 0 0 0 0 0
Lavun 0 0 0 0 0 0 0 0
Lere 0 0 0 0 0 0 0 0
Logo 0 0 0 0 0 0 0 0
Lokoja 0 0 0 0 0 0 0 0
Machina 0 0 0 0 0 0 0 0
Madagali 0 0 0 0 0 0 0 0
Madobi 0 0 0 0 0 0 0 0
Mafa 0 0 0 0 0 0 0 0
Magama 0 0 0 0 0 0 0 0
Magumeri 0 0 0 0 0 0 0 0
Mai'adua 0 0 0 0 0 0 0 0
Maiduguri 0 0 0 0 0 0 0 0
Maigatari 0 0 0 0 0 0 0 0
Maiha 0 0 0 0 0 0 0 0
Maiyama 0 0 0 0 0 0 0 0
Makoda 0 0 0 0 0 0 0 0
Makurdi 0 0 0 0 0 0 0 0
Malam Madori 0 0 0 0 0 0 0 0
Malumfashi 0 0 0 0 0 0 0 0
Mangu 0 0 0 0 0 0 0 0
Mani 0 0 0 0 0 0 0 0
Maradun 0 0 0 0 0 0 0 0
Mariga 0 0 0 0 0 0 0 0
Markafi 0 0 0 0 0 0 0 0
Marte 0 0 0 0 0 0 0 0
Maru 0 0 0 0 0 0 0 0
Mashegu 0 0 0 0 0 0 0 0
Mashi 0 0 0 0 0 0 0 0
Matazu 0 0 0 0 0 0 0 0
Mayo-Belwa 0 0 0 0 0 0 0 0
Mbaitoli 0 0 1 0 0 0 0 0
Mbo 0 0 0 0 0 0 0 0
Michika 0 0 0 0 0 0 0 0
Miga 0 0 0 0 0 0 0 0
Mikang 0 0 0 0 0 0 0 0
Minjibir 0 0 0 0 0 0 0 0
Misau 0 0 0 0 0 0 0 0
Mkpat Enin 0 0 0 0 0 0 0 0
Moba 0 0 0 0 0 0 0 0
Mobbar 0 0 0 0 0 0 0 0
Mokwa 0 0 0 0 0 0 0 0
Monguno 0 0 0 0 0 0 0 0
Mopa-Muro 0 0 0 0 0 0 0 0
Moro 0 0 0 0 0 0 0 0
Mubi North 0 0 0 0 0 0 0 0
Mubi South 0 0 0 0 0 0 0 0
Musawa 0 0 0 0 0 0 0 0
Mushin 0 0 0 0 0 0 0 0
Muya 0 0 0 0 0 0 0 0
Nafada 0 0 0 0 0 0 0 0
Nangere 0 0 0 0 0 0 0 0
Nasarawa 0 0 0 0 0 0 0 0
Nasarawa-Eggon 0 0 0 0 0 0 0 0
Ndokwa East 0 0 0 0 0 0 0 0
Ndokwa West 0 0 0 0 0 0 0 0
Nembe 0 0 0 0 0 0 0 0
Ngala 0 0 0 0 0 0 0 0
Nganzai 0 0 0 0 0 0 0 0
Ngaski 0 0 0 0 0 0 0 0
Ngor-Okpala 0 1 0 0 0 0 0 0
Nguru 0 0 0 0 0 0 0 0
Ningi 0 0 0 0 0 0 0 0
Njaba 0 0 0 0 0 0 0 0
Njikoka 0 0 0 0 0 0 0 0
Nkanu East 0 0 0 0 0 0 0 0
Nkanu West 0 0 0 0 0 0 0 0
Nkwerre 1 0 0 0 0 0 0 0
Nnewi North 0 0 0 0 0 0 0 0
Nnewi South 0 1 0 0 0 0 0 0
Nsit Atai 0 0 0 0 0 0 0 0
Nsit Ibom 0 0 0 0 0 0 0 0
Nsit Ubium 0 0 0 0 0 0 0 0
Nsukka 0 0 0 0 0 0 0 0
Numan 0 0 0 0 0 0 0 0
Nwangele 0 0 0 1 0 0 0 0
Obafemi-Owode 0 0 0 0 0 0 0 0
Obanliku 0 0 0 0 0 0 0 0
Obi 0 0 0 0 0 0 0 0
Obi Ngwa 0 1 0 0 0 0 0 0
Obia/Akpor 0 0 0 0 0 0 0 0
Obokun 0 0 0 0 0 0 0 0
Obot Akara 0 0 0 0 0 0 0 0
Obowo 0 0 0 0 0 1 0 0
Obubra 0 0 0 0 0 0 0 0
Obudu 0 0 0 0 0 0 0 0
Odeda 0 0 0 0 0 0 0 0
Odigbo 0 0 0 0 0 0 0 0
Odo-Otin 0 0 0 0 0 0 0 0
Odogbolu 0 0 0 0 0 0 0 0
Odukpani 0 0 0 0 0 0 0 0
Offa 0 0 0 0 0 0 0 0
Ofu 0 0 0 0 0 0 0 0
Ogba/Egbema/Ndoni 0 0 0 0 0 0 0 0
Ogbadibo 0 0 0 0 0 0 0 0
Ogbaru 0 0 0 0 0 0 0 0
Ogbia 0 0 0 0 0 0 0 0
Ogbomosho North 0 0 0 0 0 0 0 0
Ogbomosho South 0 0 0 0 0 0 0 0
Ogo Oluwa 0 0 0 0 0 0 0 0
Ogoja 0 0 0 0 0 0 0 0
Ogori/Magongo 0 0 0 0 0 0 0 0
Ogu/Bolo 0 0 0 0 0 0 0 0
Ogun waterside 0 0 0 0 0 0 0 0
Oguta 0 0 0 0 0 0 0 0
Ohafia 0 0 0 0 0 0 0 0
Ohaji/Egbema 0 0 0 0 0 0 0 0
Ohaozara 0 0 0 0 0 0 0 0
Ohaukwu 0 0 0 0 0 0 0 0
Ohimini 0 0 0 0 0 0 0 0
Oji-River 1 0 0 0 0 0 0 0
Ojo 0 0 0 0 0 0 0 0
Oju 0 0 0 0 0 0 0 0
Oke-Ero 0 0 0 0 0 0 0 0
Okehi 0 0 0 0 0 0 0 0
Okene 0 0 0 0 0 0 0 0
Okigwe 0 0 0 0 0 0 0 1
Okitipupa 0 0 0 0 0 0 0 0
Okobo 0 0 0 0 0 0 0 0
Okpe 0 0 0 0 0 0 0 0
Okpokwu 0 0 0 0 0 0 0 0
Okrika 0 0 0 0 0 0 0 0
Ola-oluwa 0 0 0 0 0 0 0 0
Olamabolo 0 0 0 0 0 0 0 0
Olorunda 0 0 0 0 0 0 0 0
Olorunsogo 0 0 0 0 0 0 0 0
Oluyole 0 0 0 0 0 0 0 0
Omala 0 0 0 0 0 0 0 0
Omumma 0 0 0 0 0 0 0 0
Ona-Ara 0 0 0 0 0 0 0 0
Ondo East 0 0 0 0 0 0 0 0
Ondo West 0 0 0 0 0 0 0 0
Onicha 0 0 0 0 0 0 0 0
Onitsha North 0 0 0 0 0 0 0 0
Onitsha South 0 0 0 0 0 0 0 0
Onna 0 0 0 0 0 0 0 0
Opobo/Nkoro 0 0 0 0 0 0 0 0
Oredo 0 0 0 0 0 0 0 0
Orelope 0 0 0 0 0 0 0 0
Orhionmwon 0 0 0 0 0 0 0 0
Ori Ire 0 0 0 0 0 0 0 0
Oriade 0 0 0 0 0 0 0 0
Orlu 0 1 0 0 0 0 0 0
Orolu 0 0 0 0 0 0 0 0
Oron 0 0 0 0 0 0 0 0
Orsu 0 0 0 0 0 0 0 0
Oru East 0 0 0 0 0 0 0 0
Oru West 0 0 0 0 0 0 0 0
Oruk Anam 0 0 0 0 0 0 0 0
Orumba North 0 1 0 0 0 0 0 0
Orumba South 0 0 0 0 1 0 0 0
Ose 0 0 0 0 0 0 0 0
Oshimili North 0 0 0 0 0 0 0 0
Oshimili South 0 0 0 0 0 0 0 0
Oshodi-Isolo 0 0 0 0 0 0 0 0
Osisioma Ngwa 0 0 1 0 0 0 0 0
Osogbo 0 0 0 0 0 0 0 0
Oturkpo 0 0 0 0 0 0 0 0
Ovia North East 0 0 0 0 0 0 0 0
Ovia South West 0 0 0 0 0 0 0 0
Owan East 0 0 0 0 0 0 0 0
Owan West 0 0 0 0 0 0 0 0
Owerri-Municipal 0 0 0 0 0 0 0 0
Owerri North 0 0 0 0 0 0 0 0
Owerri West 0 0 0 0 0 0 0 0
Owo 0 0 0 0 0 0 0 0
Oye 0 0 0 0 0 0 0 0
Oyi 0 0 0 0 0 0 0 0
Oyigbo 0 0 0 0 0 0 0 0
Oyo East 0 0 0 0 0 0 0 0
Oyo West 0 0 0 0 0 0 0 0
Oyun 0 0 0 0 0 0 0 0
Paikoro 0 0 0 0 0 0 0 0
Pankshin 0 0 0 0 0 0 0 0
Patani 0 0 0 0 0 0 0 0
Pategi 0 0 0 0 0 0 0 0
Port-Harcourt 0 0 0 0 0 0 0 0
Potiskum 0 0 0 0 0 0 0 0
Qua'an Pan 0 0 0 0 0 0 0 0
Rabah 0 0 0 0 0 0 0 0
Rafi 0 0 0 0 0 0 0 0
Rano 0 0 0 0 0 0 0 0
Remo North 0 0 0 0 0 0 0 0
Rijau 0 0 0 0 0 0 0 0
Rimi 0 0 0 0 0 0 0 0
Rimin Gado 0 0 0 0 0 0 0 0
Ringim 0 0 0 0 0 0 0 0
Riyom 0 0 0 0 0 0 0 0
Rogo 0 0 0 0 0 0 0 0
Roni 0 0 0 0 0 0 0 0
Sabon-Gari 0 0 0 0 0 0 0 0
Sabon Birni 0 0 0 0 0 0 0 0
Sabuwa 0 0 0 0 0 0 0 0
Safana 0 0 0 0 0 0 0 0
Sagbama 0 0 0 0 0 0 0 0
Sakaba 0 0 0 0 0 0 0 0
Saki East 0 0 0 0 0 0 0 0
Saki West 0 0 0 0 0 0 0 0
Sandamu 0 0 0 0 0 0 0 0
Sanga 0 0 0 0 0 0 0 0
Sapele 0 0 0 0 0 0 0 0
Sardauna 0 0 0 0 0 0 0 0
Shagamu 0 0 0 0 0 0 0 0
Shagari 0 0 0 0 0 0 0 0
Shanga 0 0 0 0 0 0 0 0
Shani 0 0 0 0 0 0 0 0
Shanono 0 0 0 0 0 0 0 0
Shelleng 0 0 0 0 0 0 0 0
Shendam 0 0 0 0 0 0 0 0
Shinkafi 0 0 0 0 0 0 0 0
Shira 0 0 0 0 0 0 0 0
Shiroro 0 0 0 0 0 0 0 0
Shomgom 0 0 0 0 0 0 0 0
Shomolu 0 0 0 0 0 0 0 0
Silame 0 0 0 0 0 0 0 0
Soba 0 0 0 0 0 0 0 0
Sokoto North 0 0 0 0 0 0 0 0
Sokoto South 0 0 0 0 0 0 0 0
Song 0 0 0 0 0 0 0 0
Southern Ijaw 0 0 0 0 0 0 0 0
Sule-Tankarkar 0 0 0 0 0 0 0 0
Suleja 0 0 0 0 0 0 0 0
Sumaila 0 0 0 0 0 0 0 0
Suru 0 0 0 0 0 0 0 0
Surulere 0 0 0 0 0 0 0 0
Tafa 0 0 0 0 0 0 0 0
Tafawa-Balewa 0 0 0 0 0 0 0 0
Tai 0 0 0 0 0 0 0 0
Takai 0 0 0 0 0 0 0 0
Takum 0 0 0 0 0 0 0 0
Talata Mafara 0 0 0 0 0 0 0 0
Tambuwal 0 0 0 0 0 0 0 0
Tangaza 0 0 0 0 0 0 0 0
Tarauni 0 0 0 0 0 0 0 0
Tarka 0 0 0 0 0 0 0 0
Tarmua 0 0 0 0 0 0 0 0
Taura 0 0 0 0 0 0 0 0
Tofa 0 0 0 0 0 0 0 0
Toro 0 0 0 0 0 0 0 0
Toto 0 0 0 0 0 0 0 0
Toungo 0 0 0 0 0 0 0 0
Tsafe 0 0 0 0 0 0 0 0
Tsanyawa 0 0 0 0 0 0 0 0
Tudun Wada 0 0 0 0 0 0 0 0
Tureta 0 0 0 0 0 0 0 0
Udenu 0 0 0 0 0 0 0 0
Udi 0 0 0 0 0 0 0 0
Udu 0 0 0 0 0 0 0 0
Udung Uko 0 0 0 0 0 0 0 0
Ughelli North 0 0 0 0 0 0 0 0
Ughelli South 0 0 0 0 0 0 0 0
Ugwunagbo 0 0 0 0 0 0 0 0
Uhunmwonde 0 0 0 0 0 0 0 0
Ukanafun 0 0 0 0 0 0 0 0
Ukum 0 0 0 0 0 0 0 0
Ukwa East 0 0 0 0 0 0 0 0
Ukwa West 0 0 0 0 0 0 0 0
Ukwuani 0 0 0 0 0 0 0 0
Umu-Nneochi 0 0 0 0 0 1 0 0
Umuahia North 0 0 0 1 0 0 0 0
Umuahia South 0 0 1 0 0 0 0 0
Ungogo 0 0 0 0 0 0 0 0
Unuimo 0 0 0 0 1 0 0 0
Uruan 0 0 0 0 0 0 0 0
Urue-Offong/Oruko 0 0 0 0 0 0 0 0
Ushongo 0 0 0 0 0 0 0 0
Ussa 0 0 0 0 0 0 0 0
Uvwie 0 0 0 0 0 0 0 0
Uyo 0 0 0 0 0 0 0 0
Uzo-Uwani 0 0 0 0 0 0 0 0
Vandeikya 0 0 0 0 0 0 0 0
Wamako 0 0 0 0 0 0 0 0
Wamba 0 0 0 0 0 0 0 0
Warawa 0 0 0 0 0 0 0 0
Warji 0 0 0 0 0 0 0 0
Warri North 0 0 0 0 0 0 0 0
Warri South 0 0 0 0 0 0 0 0
Warri South West 0 0 0 0 0 0 0 0
Wasagu/Danko 0 0 0 0 0 0 0 0
Wase 0 0 0 0 0 0 0 0
Wudil 0 0 0 0 0 0 0 0
Wukari 0 0 0 0 0 0 0 0
Wurno 0 0 0 0 0 0 0 0
Wushishi 0 0 0 0 0 0 0 0
Yabo 0 0 0 0 0 0 0 0
Yagba East 0 0 0 0 0 0 0 0
Yagba West 0 0 0 0 0 0 0 0
Yakurr 0 0 0 0 0 0 0 0
Yala 0 0 0 0 0 0 0 0
Yamaltu/Deba 0 0 0 0 0 0 0 0
Yankwashi 0 0 0 0 0 0 0 0
Yauri 0 0 0 0 0 0 0 0
Yenegoa 0 0 0 0 0 0 0 0
Yola North 0 0 0 0 0 0 0 0
Yola South 0 0 0 0 0 0 0 0
Yorro 0 0 0 0 0 0 0 0
Yunusari 0 0 0 0 0 0 0 0
Yusufari 0 0 0 0 0 0 0 0
Zaki 0 0 0 0 0 0 0 0
Zango 0 0 0 0 0 0 0 0
Zango-Kataf 0 0 0 0 0 0 0 0
Zaria 0 0 0 0 0 0 0 0
Zing 0 0 0 0 0 0 0 0
Zurmi 0 0 0 0 0 0 0 0
Zuru 0 0 0 0 0 0 0 0To get the number of disconnected connected subgraphs in the wmd72weight matrix, we can use the n.comp.nb() function of the spdep package.
n_comp <- n.comp.nb(wm_d72)
n_comp$nc[1] 2The results confirms that there is an isolated island in the result,
we can plot the map of neighbours by using the plot function to determine if the generated map is of good use.
plot(nga_wp$geometry, border="lightblue")
plot(wm_d72, coord, add=TRUE)
plot(nga_wp$geometry, border="lightblue")
plot(k1, coord, add=TRUE, col="red", length = 0.1)
The black lines show the links of neighbours within the cut-off distance of 72km.
The red lines show the links of 1st nearest neighbours
It looks like it is not wise to use fixed weight distance matrix as some LGAs has alot of neighbours within 72km, it could really skew the spatial correlation giving them more weights to these areas based on the resultant map. We will work with adaptive distance weight matrix instead.
Adaptive Weight distance matrix
By enforcing symmetry or accepting asymmetric neighbours, as shown in the code below, it is possible to control the number of neighbours of each region using the k nearest neighbour (knn) algorithm. In this case we set k to 8 as discussed in class. We use str() to display the result
knn8 = knn2nb(knearneigh(coord, k=8))
str(knn8)List of 774
$ : int [1:8] 2 321 364 548 597 624 721 725
$ : int [1:8] 1 321 548 597 624 721 725 726
$ : int [1:8] 250 261 447 477 492 507 509 526
$ : int [1:8] 12 20 257 263 446 454 466 690
$ : int [1:8] 203 208 286 288 331 334 539 738
$ : int [1:8] 170 217 218 337 379 553 577 601
$ : int [1:8] 8 176 214 281 283 306 544 555
$ : int [1:8] 7 214 281 306 327 544 555 651
$ : int [1:8] 18 19 218 337 574 576 601 757
$ : int [1:8] 25 216 325 364 365 528 552 632
$ : int [1:8] 26 27 43 68 191 524 565 762
$ : int [1:8] 135 263 417 429 446 454 690 695
$ : int [1:8] 31 37 211 320 393 570 583 584
$ : int [1:8] 170 363 379 546 563 577 581 589
$ : int [1:8] 22 49 82 177 297 306 580 623
$ : int [1:8] 30 187 188 296 328 357 360 635
$ : int [1:8] 35 275 295 378 460 591 638 639
$ : int [1:8] 9 19 218 376 574 576 601 757
$ : int [1:8] 9 18 56 103 376 574 576 601
$ : int [1:8] 4 106 125 239 263 419 454 466
$ : int [1:8] 60 61 162 269 520 578 596 626
$ : int [1:8] 49 297 326 443 515 623 682 693
$ : int [1:8] 54 291 292 536 537 614 618 619
$ : int [1:8] 84 123 437 476 527 652 673 761
$ : int [1:8] 10 181 216 314 325 366 552 730
$ : int [1:8] 11 27 68 191 336 439 562 762
$ : int [1:8] 11 26 191 439 562 565 663 762
$ : int [1:8] 29 178 299 300 301 358 369 598
$ : int [1:8] 172 173 178 182 358 378 460 591
$ : int [1:8] 16 39 41 186 192 329 357 360
$ : int [1:8] 13 37 211 289 561 570 583 584
$ : int [1:8] 51 62 461 462 515 623 682 693
$ : int [1:8] 47 166 227 238 242 655 743 750
$ : int [1:8] 42 104 136 137 213 553 559 757
$ : int [1:8] 17 275 276 277 278 279 295 460
$ : int [1:8] 50 107 247 408 432 455 681 759
$ : int [1:8] 38 40 211 212 570 583 584 629
$ : int [1:8] 30 39 40 41 186 192 320 570
$ : int [1:8] 30 38 40 41 186 192 320 329
$ : int [1:8] 37 38 39 41 186 192 320 570
$ : int [1:8] 30 38 39 40 186 192 360 634
$ : int [1:8] 86 136 137 499 587 613 718 734
$ : int [1:8] 11 68 157 524 549 565 590 645
$ : int [1:8] 16 45 192 290 303 328 360 634
$ : int [1:8] 44 187 290 303 328 341 360 599
$ : int [1:8] 387 417 429 438 459 521 668 742
$ : int [1:8] 33 111 166 234 238 691 698 750
$ : int [1:8] 65 113 265 386 407 428 482 701
$ : int [1:8] 22 32 297 326 515 623 682 693
$ : int [1:8] 36 98 107 247 409 416 432 681
$ : int [1:8] 32 62 461 462 515 580 623 693
$ : int [1:8] 53 78 165 293 532 602 603 636
$ : int [1:8] 52 78 80 165 280 602 621 636
$ : int [1:8] 23 79 293 294 532 536 537 618
$ : int [1:8] 122 169 246 333 430 571 605 697
$ : int [1:8] 77 368 376 533 534 576 601 728
$ : int [1:8] 53 58 199 312 322 323 621 622
$ : int [1:8] 57 322 323 564 602 603 621 622
$ : int [1:8] 88 128 129 259 493 700 714 748
$ : int [1:8] 61 158 563 578 589 592 596 626
$ : int [1:8] 21 60 269 578 589 592 596 626
$ : int [1:8] 32 51 461 462 515 623 682 693
$ : int [1:8] 90 237 384 416 467 497 765 772
$ : int [1:8] 48 65 74 113 131 265 386 407
$ : int [1:8] 48 64 74 113 265 407 683 701
$ : int [1:8] 19 103 104 288 331 338 351 574
$ : int [1:8] 347 348 560 566 567 609 640 694
$ : int [1:8] 11 43 157 190 191 549 590 645
$ : int [1:8] 140 146 248 274 473 500 512 513
$ : int [1:8] 71 298 299 301 341 343 344 610
$ : int [1:8] 70 172 173 298 299 343 344 625
$ : int [1:8] 17 361 566 567 568 609 638 639
$ : int [1:8] 72 361 374 377 404 607 665 666
$ : int [1:8] 65 109 113 251 265 683 741 754
$ : int [1:8] 110 272 398 422 433 485 501 768
$ : int [1:8] 254 287 427 459 470 547 647 677
$ : int [1:8] 56 195 533 534 579 618 619 728
$ : int [1:8] 52 54 79 80 165 215 532 636
$ : int [1:8] 54 78 165 293 532 579 618 636
$ : int [1:8] 52 53 78 165 215 280 636 739
$ : int [1:8] 99 145 227 233 426 483 689 760
$ : int [1:8] 15 32 49 51 177 352 580 623
$ : int [1:8] 132 258 383 414 433 529 767 768
$ : int [1:8] 24 131 148 386 437 482 673 692
$ : int [1:8] 105 156 267 394 654 675 707 712
$ : int [1:8] 42 136 137 499 587 613 718 734
$ : int [1:8] 149 151 221 226 399 410 486 657
$ : int [1:8] 59 116 128 150 489 648 700 714
$ : int [1:8] 260 408 463 542 674 676 681 759
$ : int [1:8] 63 163 236 237 384 452 710 765
$ : int [1:8] 160 271 388 406 473 475 492 525
$ : int [1:8] 95 119 390 391 392 423 487 656
$ : int [1:8] 73 354 374 402 594 607 665 666
$ : int [1:8] 13 31 60 158 436 561 596 709
$ : int [1:8] 92 390 391 392 405 423 469 656
$ : int [1:8] 97 139 389 403 420 451 488 653
$ : int [1:8] 96 168 389 420 451 653 662 773
$ : int [1:8] 50 117 153 231 409 432 696 708
$ : int [1:8] 81 145 426 483 667 689 760 769
[list output truncated]
- attr(*, "region.id")= chr [1:774] "1" "2" "3" "4" ...
- attr(*, "call")= language knearneigh(x = coord, k = 8)
- attr(*, "sym")= logi FALSE
- attr(*, "type")= chr "knn"
- attr(*, "knn-k")= num 8
- attr(*, "class")= chr "nb"Plotting distance based neighbours
plot(nga_wp$geometry, border="lightblue")
plot(knn8, coord, pch = 19, cex = 0.6, add = TRUE, col = "red")
knn8Neighbour list object:
Number of regions: 774
Number of nonzero links: 6192
Percentage nonzero weights: 1.033592
Average number of links: 8
Non-symmetric neighbours listThis is a much better plot than the fixed distance matrix, as symmetry is enforced.
Computing Spatial Autocorrelation
Now that we are ready we our weight matrix, we can begin to compute spatial autocorrelation We will start with GLOBAL spatial autocorrelation statistics and to perform spatial complete randomness test for global spatial correlation
Computing Spatial Autocorrelation: Moran's I
Moran’s I - The Null Hypothesis
The null hypothesis is to assume that non functional water points are randomly distributed between the different LGAs.
Computing Moran’s I
We will perform Moran's I statistical test with moran.test() of the spdep package. We will need to convert the knn8 weight matrix to a listw object first using nb2listw()
knn8ListW = nb2listw(knn8)
moran.test(nga_wp$`wpt non functional`, listw = knn8ListW, zero.policy = TRUE, na.action = na.omit)
Moran I test under randomisation
data: nga_wp$`wpt non functional`
weights: knn8ListW
Moran I statistic standard deviate = 22.673, p-value < 2.2e-16
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
0.3822703126 -0.0012936611 0.0002861956 Based on the result, we will reject the null hypothesis as the p-value is less than 0.05. In fact as the p-value is less than 0.01, we can consider that as highly significant. The non functional water points are spatially clustered based on Moran's I statistics and are not random.
Computing Spatial Autocorrelation: Moran's I with Monte Carlo simulation
In order to confirm that the null hypothesis is false, we could use Monte Carlo simulation to predict potential outcomes of the event by using moran.mc() function of the spdep package. We will use 1000 simulations for this test and are not random.
set.seed(1234)
global_moran_mc = moran.mc(nga_wp$`wpt non functional`, listw = knn8ListW, nsim=999, zero.policy = TRUE, na.action = na.omit)
global_moran_mc
Monte-Carlo simulation of Moran I
data: nga_wp$`wpt non functional`
weights: knn8ListW
number of simulations + 1: 1000
statistic = 0.38227, observed rank = 1000, p-value = 0.001
alternative hypothesis: greaterBased on the simulation result, we will reject the null hypothesis as the p-value is less than 0.05. In fact as the p-value is 0.01, we can consider that as highly significant.
The non functional water points in the LGAs are not randomly distributed. Thus, since the Moran I statistics (0.38) is greater than 0, they are spatially clustered.
Visualizing Monte Carlo Moran's I
We will examine the simulated Moran's I test statistics in detail. This can be done by computing the mean, variance and standard deviation and summary statistics
mean(global_moran_mc$res[1:999]) #compute mean[1] -0.000628295var(global_moran_mc$res[1:999]) #compute variance[1] 0.0002988427sd(global_moran_mc$res[1:999]) #compute std dev.[1] 0.01728707summary(global_moran_mc$res[1:999]) Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.0570637 -0.0128308 -0.0011785 -0.0006283 0.0106868 0.0645272 We can plot the statistical values as a histogram using ggplot, however we need to convert the result into a data frame first
df = data.frame(global_moran_mc$res) #convert to data frame
ggplot(df, aes(global_moran_mc$res)) + #aes = column name
geom_histogram(bins=100,
color="White",
fill="lightblue") +
labs(x = "Simulated Moran's I",
y = "Frequency") +
xlim(-0.07, 0.07) + ylim(0, 50) +
geom_vline(aes(xintercept=0),
color="red", linetype="dashed", size=1)
Based on the histogram, we can conclude that there is a positive correlation based on the result of the histogram for Moran's I Statistics, i.e. non functional water points in NGAs are clustered, and not disperse
Computing Spatial Autocorrelation: Geary's C
Geary’s C - The Null Hypothesis
The null hypothesis is to assume that non functional water points are clustering for either similar or dissimilar values are random.
Computing Geary’s C
We will perform Geary's C statistical test with geary.test() of the spdep package.
geary.test(nga_wp$`wpt non functional`, listw = knn8ListW)
Geary C test under randomisation
data: nga_wp$`wpt non functional`
weights: knn8ListW
Geary C statistic standard deviate = 19.315, p-value < 2.2e-16
alternative hypothesis: Expectation greater than statistic
sample estimates:
Geary C statistic Expectation Variance
0.6066280275 1.0000000000 0.0004147634 Based on the result, we will reject the null hypothesis as the p-value is less than 0.05. In fact as the p-value is less than 0.01, we can consider that as highly significant.
Therefore, we can conclude that the non functional water points are clustering for either similar or dissimilar values, they are not randomly distributed based on Geary's C statistics
Computing Spatial Autocorrelation: Geary’s C with Monte Carlo simulation
In order to further confirm that the null hypothesis is false, we could use Monte Carlo simulation to predict potential outcomes of the event by using geary.mc() function of the spdep package. We will use 1000 simulations for this test
set.seed(1234)
global_geary_mc = geary.mc(nga_wp$`wpt non functional`, listw = knn8ListW, nsim=999)
global_geary_mc
Monte-Carlo simulation of Geary C
data: nga_wp$`wpt non functional`
weights: knn8ListW
number of simulations + 1: 1000
statistic = 0.60663, observed rank = 1, p-value = 0.001
alternative hypothesis: greaterBased on the simulation result, we will reject the null hypothesis as the p-value is less than 0.05. In fact as the p-value is 0.01, we can consider that as highly significant.
The non functional water points in the LGAs are clustering for either similar or dissimilar values, they are not randomly distributed. Thus, since the Geary’s C statistics (0.61) is lesser than 1, they are spatially clustered.
Visualizing Monte Carlo Geary’s C
We will examine the simulated Geary’s C test statistics in detail. This can be done by computing the mean, variance and standard deviation and summary statistics
mean(global_geary_mc$res[1:999]) #compute mean[1] 0.9991838var(global_geary_mc$res[1:999]) #compute variance[1] 0.0004175843sd(global_geary_mc$res[1:999]) #compute std dev.[1] 0.02043488summary(global_geary_mc$res[1:999]) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.9276 0.9859 0.9996 0.9992 1.0132 1.0699 We can plot the statistical values as a histogram using ggplot, however we need to convert the result into a data frame first
df_G = data.frame(global_geary_mc$res) #convert to data frame
ggplot(df_G, aes(global_geary_mc$res)) + #aes = column name
geom_histogram(bins=100,
color="White",
fill="lightblue") +
labs(x = "Simulated Geary's C",
y = "Frequency") +
xlim(0.9, 1.1) + ylim(0, 50) +
geom_vline(aes(xintercept=1),
color="red", linetype="dashed", size=1)
Based on the histogram, we can conclude that there is a positive correlation based on the result of the histogram for Geary's C Statistics, i.e. non functional water points in NGAs are clustered
It is interesting to note that in Moran I the smaller the number, indicates negative correlation (small -> -ve), in contrast in Geary's C the smaller the number indicates positive correlation (small -> +ve)
Spatial Correlogram
Reference
Kassambara A (n.d) . K-Means Clustering in R: Algorithm and Practical Examples
https://www.datanovia.com/en/lessons/k-means-clustering-in-r-algorith-and-practical-examples/
Long, A (n.d.), Local Moran
http://ceadserv1.nku.edu/longa//geomed/stats/localmoran/localmoran.html